A CD45-based barcoding approach to multiplex mass-cytometry (CyTOF).

Abstract

CyTOF enables the study of the immune system with a complexity, depth, and multidimensionality never achieved before. However, the full potential of using CyTOF can be limited by scarce cell samples. Barcoding strategies developed based on direct labeling of cells using maleimido-monoamide-DOTA (m-DOTA) provide a very useful tool. However, using m-DOTA has some inherent problems, mainly associated with signal intensity. This may be a source of uncertainty when samples are multiplexed. As an alternative or complementary approach to m-DOTA, conjugating an antibody, specific for a membrane protein present on most immune cells, with different isotopes could address the issues of stability and signal intensity needed for effective barcoding. We chose for this purpose CD45, and designed experiments to address different types of cultures and the ability to detect extra- and intra-cellular targets. We show here that our approach provides an useful alternative to m-DOTA in terms of sensitivity, specificity, flexibility, and user-friendliness. Our manuscript provides details to effectively barcode immune cells, overcoming limitations in current technology and enabling the use of CyTOF with scarce samples (for instance precious clinical samples).

KEYWORDS:

Ability of CD45 versus m-DOTA barcoding in subgroups differentiation in different channels. A: After singlets, live cells gating, the population positive to each isotopes. B: Cell number and population ratio of each subgroup in two barcoding methods. Data generated by Boolean gating in FlowJo. C: Log value of average signal intensity (five independent experiments) comparison between CD45 and m-DOTA barcodes in Ho165 and Lu175. CD45 intensity is 30× higher (P = 0.00021 in Ho165, P = 0.00014 in Lu175). The error bar (one standard deviation) in CD45 is consistently lower than mDOTA. D: Comparison of ratio of actual subgroup distribution versus ideal distribution between CD45 and mDOTA (P = 0.0022). Subgroup ratio is a function of actual subgroup proportion generated by Boolean gating divided by ideal proportion, that is, if n represents the number of equal population in an experiment, ideal proportion is 1/n.

Efficacy in differentiating of subgroups. A: Three distinct PBMC populations were gated by two out of four CD45 barcodes. B: The population negative to two CD45 barcodes in (A) was further gated by another two CD45 barcodes. C, D: CD8, CXCR3, TNFα, IFNγ, IL-2, and IL-4 were gated from nonstimulated and stimulated population, respectively. E: Panel used for CD45 barcodes and targets labeling. F: Resolution of ten different cell populations employing four barcodes. G: Multiplexing strategy using four CD45 barcodes.

Diagram illustrates the use of automated gating in R. A, B: Scatter plots of CD45A versus CD45C and CD45B versus CD45D. The red and blue lines are auto-generated gates based on R. Each individual population has been gated out effectively by R auto generated gates. The density plots demonstrate how the auto gates are generated in R. The valley between the two peaks is detected and used as the reference cutoff for gating. C: Bar chart compares subgroup percentages generated by FlowJo Boonlean gating to R auto debarcoding for five subjects with stimulation (S) and without stimulation (NoS). R debarcoding is robust and can generate similar results as FlowJo.